Ecological studies can be limited by the mismatch in spatial-temporal scales between wildlife GPS telemetry data, collected sub-hourly, and the large-area maps used to identify disturbances, generally updated annually. Recent advancements in remote sensing, data fusion modeling, mapping, and change detection approaches offer environmental data products representing every 16-day period through the growing season. Here we highlight opportunities and challenges for integrating wildlife location data with high spatial and temporal resolution landscape disturbance data sets, available from remotely sensed imagery. We integrated 16-day outputs from the Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH) disturbance maps with grizzly bear (Ursus arctos) telemetry data. Our results indicate that males and females avoided same-year disturbances, while male bears were most likely to avoid recently disturbed areas in summer. When intra-year (disturbances mapped at a 16-day timestep) analysis of disturbance was compared to traditional annual time-step analysis, annual aggregation of disturbance data resulted in an increase in the observed selection of same-year disturbed habitat, although change was not statistically significant (α 0.05). We caution the use of low-temporal resolution disturbance data to evaluate short-term impacts on wildlife and highlight the need for further development of probabilistic- and model-based techniques for overcoming spatialtemporal differences between datasets.

Additional Metadata
ISBN 978-3-319-47035-1
Persistent URL dx.doi.org/10.1007/978-3-319-47037-5_16
Series Remote Sensing and Digital Image Processing
Citation
Brown, N.D.A. (Nicholas D. A.), Nelson, T. (Trisalyn), Wulder, M.A. (Michael A.), Coops, N.C. (Nicholas C.), Hilker, T. (Thomas), Bater, C.W. (Christopher W.), … Stenhouse, G.B. (Gordon B.). (2016). An approach for determining relationships between disturbance and habitat selection using bi-weekly synthetic images and telemetry data. In Multitemporal Remote Sensing (pp. 341–356). doi:10.1007/978-3-319-47037-5_16